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Center for Nonlinear Studies |
Since 1950s’ aerospace programs have seen remarkable success, mainly due to the model based control theory. Until recently, there was no interaction between the model based control theory and neuroscience. The advent of nonlinear ensemble Kalman filter with improved neuronal models offers a paradigm shifting improvement in our ability to observe, predict, and control the state of neuronal systems. We have recently used an unscented Kalman filter to predict hidden states and future trajectories in neuronal systems, reconstruct ion dynamics that modulate excitability, control neuronal activity through a variety of control variables including a novel strategy for dynamic conductance clamping, and show the feasibility of controlling pathological patterns of cellular activity such as seizures. I will begin with a neuronal network model for spontaneous seizures followed by implementation of this model into Kalman filter framework to track the experimentally inaccessible variables and parameters from single variable measurements. Finally, I will show how this framework can be used to design control strategies for neuronal networks with access to the full dynamics of the system.
Anyone interested in meeting with Dr. Ullah please contact John Pearson at pearson@lanl.gov, office: 505-667-7585 or cell: 505-412-3118
Host: John Pearson